Resampling Approaches to Complex Psychological Experiments.

Abstract

The bootstrap is a relatively new technique. In using it, the analyst intensively examines the data actually gathered to estimate the precision of the sample statistic, rather than relying on a parametric theory. While this makes little sense when parametric theories are available (such as is the case with the correlation coefficient, the mean and most other common statistics), it is a useful adjunct to traditional statistical methods when these elegant methods cannot be used. An application of the bootstrap to a complex psychological analysis approach is demonstrated. The method provides variance estimates and allows the testing of nested competing models. Most importantly, it gives a preliminary idea about the variability of quite complex parameters.